Surface-Oxidized Titanium Sulfide Nanoparticles As a Conductive Polysulfide Scavenger for a High Performance Lithium-Sulfur Battery
Bibliographic record
Abstract
Theoretically, a solid redox pair of Li and S can be used for energy storage devices with high specific capacity and energy density using low-cost sulfur as the active material, making it one of the most promising candidates to replace conventional lithium ion batteries. However, the practical use of Li-S battery is hindered by persistent issues regarding the formation of soluble polysulfides (PS) during electrochemical cycles and their shuttling effect. In order to minimize the irreversible loss of the active material from the cathode and its subsequent deposition on the anode, a few noteworthy efforts have been reported, including insertion of a protection layer or addition of a functional agent that can adsorb PS on the cathode side. In this work, we report conductive titanium(III) sulfide (Ti 2 S 3 ) nanoparticles, synthesized using a simple radio frequency (RF)-powered induction thermal plasma method, as a bifunctional material to be used in the S cathode. Ti 2 S 3 nanoparticles can provide electrical conductivity to the electrode with a smaller volume compared to conventional conducting agent carbon, while simultaneously having PS adsorption adequate to mitigate the diffusion of PS from the cathode and reduce accompanying problems. We prepared Ti 2 S 3 nanoparticles agent with improved PS adsorptivity by applying simple defect chemistry. The synthesized Ti 2 S 3 was first exposed to air to be oxidized and form S defect on its surface. The surface-oxidized Ti 2 S 3 proposed in this work shows both sufficient adsorption property to PS and electronic conductivity, and thus enhance the cyclability and areal energy density of the Li-S battery. The cyclic performance of our Li-S cell was achieved up to ~800 mAh g S -1 even after 100 cycles at relatively high S loading of 3.0 mg S cm -2 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".